Wind tunnel tests can be conducted utilizing phased microphone arrays. A phased microphone array is typically configured as a group of microphones arranged in an optimized pattern. The signals from each microphone can be sampled and then processed in the frequency domain. The relative phase differences seen at each microphone determines where noise sources are located. The amplification capability of the array allows detection of noise sources well below the background noise level. This makes microphone arrays particularly useful for wind tunnel evaluations of airframe noise since, in most cases, the noise produced by wings, flaps, struts and landing gear models will be lower than that of the wind tunnel environment.
The use of phased arrays of microphones in the study of aeroacoustic sources has increased significantly in recent years, particularly since the mid 1990's. The popularity of phased arrays is due in large part to the apparent clarity of array-processed results, which can reveal noise source distributions associated with, for example, wind tunnel models and full-scale aircraft. Properly utilized, such arrays are powerful tools that can extract noise source radiation information in circumstances where other measurement techniques may fail. Presentations of array measurements of aeroacoustic noise sources, however, can lend themselves to a great deal of uncertainty during interpretation. Proper interpretation requires knowledge of the principles of phased arrays and processing methodology. Even then, because of the complexity, misinterpretations of actual source distributions (and subsequent misdirection of engineering efforts) are highly likely.
Prior to the mid 1980's, processing of array microphone signals as a result of aeroacoustic studies involved time delay shifting of signals and summing in order to strengthen contributions from, and thus “focus” on, chosen locations over surfaces or positions in the flow field. Over the years, with great advances in computers, this basic “delay and sum” processing approach has been replaced by “classical beamforming” approaches involving spectral processing to form cross spectral matrices (CSM) and phase shifting using increasingly large array element numbers. Such advances have greatly increased productivity and processing flexibility, but have not changed at all the interpretation complexity of the processed array results.
Some aeroacoustic testing has involved the goal of forming a quantitative definition of different airframe noise sources spectra and directivity. Such a goal has been achieved with arrays in a rather straight-forward manner for the localized intense source of flap edge noise. For precise source localization, however, Coherent Output Power (COP) methods can be utilized by incorporating unsteady surface pressure measurements along with the array. Quantitative measurements for distributed sources of slat noise have been achieved utilizing an array and specially tailored weighting functions that matched array beampatterns with knowledge of the line source type distribution for slat noise. Similar measurements for distributed trailing edge noise and leading edge noise (e.g., due in this case to grit boundary layer tripping) have been performed along with special COP methodologies involving microphone groups.
A number of efforts have been made at analyzing and developing more effective array processing methodologies in order to more readily extract source information. Several efforts include those that better account for array resolution, ray path coherence loss, and source distribution coherence and for test rig reflections. In a simulation study of methods for improving array output, particularly for suppressing side lobe contamination, several beamforming techniques have been examined, including a cross spectral matrix (CSM) element weighting approach, a robust adaptive beamforming, and a CLEAN algorithm. The CLEAN algorithm is a deconvolution technique that was first implemented in the context of radio astronomy.
The CSM weighting approach reduces side lobes compared to classical beamforming with some overall improvement in main beam pattern resolution. The results for the adaptive beam former, used with a specific constant added to the CSM matrix diagonal to avoid instability problems, have been encouraging. The CLEAN algorithm has been found to possess the best overall performance for the simulated beamforming exercise. The CLEAN algorithm has also been examined in association with a related algorithm referred to as RELAX, utilizing experimental array calibration data for a no-flow condition.
The result of such studies involves a mixed success in separating out sources. In other studies, using the same data, two robust adaptive beamforming methods have been examined and found to be capable of providing sharp beam widths and low side lobes. It should be mentioned that the above methods, although perhaps offering promise, have not produced quantitatively accurate source amplitudes and distributions for real test cases. In the CLEAN methodology in particular, questions have been raised with regard to the practicality of the algorithm for arrays in reflective wind tunnel environments.
A method that has shown promise with wind tunnel aeroacoustic data is the Spectral Estimation Method (SEM). SEM requires that the measured CSM of the array be compared to a simulated CSM constructed by defining distributions of compact patches of sources (i.e., or source areas) over a chosen aeroacoustic region of interest. The difference between the two CSM's can be minimized utilizing a Conjugate Gradient Method. The application of positivity constraints on the source solutions had been found to be difficult. The resultant source distributions for the airframe noise cases examined are regarded as being feasible and realistic, although not unique.
As a consequence of the drawbacks associated with the foregoing methods and approaches, an effort has been made to develop a complete deconvolution approach for the mapping of acoustic sources to demystify two-dimensional and three-dimensional array results, to reduce misinterpretation, and to more accurately quantify position and strength of aeroacoustic sources. Traditional presentations of array results involve mapping (e.g., contour plotting) of array output over spatial regions. These maps do not truly represent noise source distributions, but ones that are convolved with the array response functions, which depend on array geometry, size (i.e., with respect to source position and distributions), and frequency.
The deconvolution methodology described in greater detail herein therefore can employ these processed results (e.g., array output at grid points) over the survey regions and the associated array beamforming characteristics (i.e., relating the reciprocal influence of the different grid point locations) over the same regions where the array's outputs are measured. A linear system of “N” (i.e., number of grid points in region) equations and “N” unknowns is created. These equations are solved in a straight-forward iteration approach. The end result of this effort is a unique robust deconvolution approach designed to determine the “true” noise source distribution over an aeroacoustic source region to replace the “classical beam formed” distributions. Example applications include ideal point and line noise source cases, as well as conformation with well documented experimental airframe noise studies of wing trailing and leading edge noise, slat noise, and flap edge/flap cove noise.